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Architecture: Form, Space, and Order
ARCHITECTURE THE GOLD STANDARD IN INTRODUCTORY ARCHITECTURE TEXTS, FULLY UPDATED TO REFLECT THE LATEST DEVELOPMENTS IN THE FIELD For more than forty years, the beautifully illustrated Architecture: Form, Space, and Order has served as the classic introduction to the basic vocabulary of architectural design. In this fifth edition, more recent additions to the architectural panoply illustrate how contemporary digital and building technologies have influenced the development of architectural forms and spaces and how architectural siting and design have responded to the call for more environmentally responsible buildings. It is designed to encourage critical thought and to promote a more evocative understanding of architecture. The fifth edition is updated with many new urban design and building precedents from a diverse range of cultural and geographic areasNew content focuses on the latest technology and trends in structure, construction, materials, and sustainabilityIncludes more than 800 illustrations, many hand-drawn, which demonstrate the foundations and concepts every architect must master Architecture: Form, Space, and Order distills complex concepts of design into a clear focus and brings difficult abstractions to life. It explains form and space in relation to light, view, openings, and enclosures and explores the organization of space, and the elements and relationships of circulation, as well as proportion and scale. In addition, the text’s detailed illustrations demonstrate the concepts presented and reveal the relationships between fundamental elements of architecture through the ages and across cultures.
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Practical LXC and LXD
Use Linux containers as an alternative virtualization technique to virtualize your operating system environment. This book will cover LXC’s unmatched flexibility with virtualization and LXD’s smooth user experience.Practical LXC and LXD begins by introducing you to Linux containers (LXC and LXD). You will then go through use cases based on LXC and LXD. Next, you will see the internal workings of LXC and LXD by considering the repositories and templates used. You will then learn how to integrate LXC and LXD with common virtualization and orchestration tools such as libvirt and SaltStack. Finally, you will dive into containerization and security. The book will explore some of the common problems in security and provide a case study on how containerization can help mitigate some of the operating system-level security issues in an IoT environment.What You Will LearnGet an introduction to Linux containersDiscover the basics of LXC and LXDSee use cases that can be solved with LXC and LXD – for developers, devops, and system administratorsMaster LXC and LXD repositoriesUse LXC and LXD with common virtualization and orchestration toolsConsider a containerization and security in IoT case studyWho This Book Is ForThe audience for this book should have basic knowledge of Linux and software development in general. The intended readership is primarily software developers, operations engineers, and system administrators who are interested in devops, though managers and enthusiasts will also benefit from this book.
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Building Web Applications with Python and Neo4j
About This BookDevelop a set of common applications and solutions with Neo4j and PythonSecure and deploy the Neo4j database in productionA step-by-step guide on implementing and deploying interactive Python-based web applications on graph data modelWho This Book Is ForIf you are a Python developer and want to expand your understanding of Python-based web applications over Neo4j graph data models, this is the book for you.What You Will LearnUnderstand the licensing and installation of the Neo4j database and work with its various tools and utilitiesLearn the intricacies of Cypher as a graph query languageWork with Cypher to create and modify graph data modelsIntegrate Python and Neo4j using Py2neoDevelop REST-based services over social network data using Flask and object graph models over Neo4jIntegrate Django-based web applications over graph data models using NeomodelExplore different deployment models and their applicability with existing applicationsIn DetailPy2neo is a simple and pragmatic Python library that provides access to the popular graph database Neo4j via its RESTful web service interface. This brings with it a heavily refactored core, a cleaner API, better performance, and some new idioms.You will begin with licensing and installing Neo4j, learning the fundamentals of Cypher as a graph query language, and exploring Cypher optimizations. You will discover how to integrate with various Python frameworks such as Flask and its extensions: Py2neo, Neomodel, and Django. Finally, the deployment aspects of your Python-based Neo4j applications in a production environment is also covered. By sequentially working through the steps in each chapter, you will quickly learn and master the various implementation details and integrations of Python and Neo4j, helping you to develop your use cases more quickly.
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Software Architecture
The1stEuropeanWorkshoponSoftwareArchitecture(EWSA2004)washeldin St Andrews, Scotland on 21–22 May 2004. The workshop provided an inter- tional forum for researchers and practitioners from academia and industry to discuss a wide range of topics in the area of software architecture and to jointly formulate an agenda for future research. We were pleased to continue this forum in EWSA 2005. The importance of software architecture as a fundamental area of software engineeringcontinues togrow.Inaddition todescribingtheunderlyingstructure of software systems, architectures are now being used to model and understand dynamicbehavior.Newareasofstudy,whichhavetheirrootsincontrolsystems, arebeginningtoemerge.The?eldofautonomicsrequiresanunderlyingsoftware architecture to describe the executing computation as does any control system that involves system evolution. The range of papers in EWSA 2005 re?ected both the traditional and new applications of software architecture techniques. EWSA 2005 distinguished between three types of papers: research papers (which describe authors’ novel research work), a case study (which describes experiencesrelatedtosoftwarearchitectures)andpositionpapers(whichpresent concise arguments about a topic of software architecture research or practice). TheProgrammeCommitteeselected18papers(12researchpapers,4position papers, 1 case study, and 1 unrefereed invited paper) out of 41 submissions from 20 countries (Australia, Belgium, Brazil, Chile, China, Czech Republic, Finland, France, Germany, India, Italy, Ireland, Korea, Netherlands, Pakistan, Portugal, Spain, Switzerland, UK, USA). All submissions were reviewed by at least three members of the Programme Committee. Papers were selected based on originality, quality, soundness and relevance to the workshop. Credit for the quality of the proceedings goes to all authors of papers.
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Practical System Programming with C
This book teaches system programming with the latest versions of C through a set of practical examples and problems. It covers the development of a handful of programs, implementing efficient coding examples. Practical System Programming with C contains three main parts: getting your hands dirty with multithreaded C programming; practical system programming using concepts such as processes, signals, and inter-process communication; and advanced socket-based programming which consists of developing a network application for reliable communication. You will be introduced to a marvelous ecosystem of system programming with C, from handling basic system utility commands to communicating through socket programming. With the help of socket programming you will be able to build client-server applications in no time. The “secret sauce” of this book is its curated list of topics and solutions, which fit together through a set of different pragmatic examples; each topic is covered from scratch in an easy-to-learn way. On that journey, you’ll focus on practical implementations and an outline of best practices and potential pitfalls. The book also includes a bonus chapter with a list of advanced topics and directions to grow your skills. What You Will LearnProgram with operating systems using the latest version of C Work with LinuxCarry out multithreading with C Examine the POSIX standardsWork with files, directories, processes, and signalsExplore IPC and how to work with it Who This Book Is For Programmers who have an exposure to C programming and want to learn system programming. This book will help them to learn about core concepts of operating systems with the help of C programming..
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Productive and Efficient Data Science with Python
This book focuses on the Python-based tools and techniques to help you become highly productive at all aspects of typical data science stacks such as statistical analysis, visualization, model selection, and feature engineering.You’ll review the inefficiencies and bottlenecks lurking in the daily business process and solve them with practical solutions. Automation of repetitive data science tasks is a key mindset that is promoted throughout the book. You’ll learn how to extend the existing coding practice to handle larger datasets with high efficiency with the help of advanced libraries and packages that already exist in the Python ecosystem. The book focuses on topics such as how to measure the memory footprint and execution speed of machine learning models, quality test a data science pipelines, and modularizing a data science pipeline for app development. You’ll review Python libraries which come in very handy for automating and speeding up the day-to-day tasks. In the end, you’ll understand and perform data science and machine learning tasks beyond the traditional methods and utilize the full spectrum of the Python data science ecosystem to increase productivity. What You’ll Learn Write fast and efficient code for data science and machine learningBuild robust and expressive data science pipelinesMeasure memory and CPU profile for machine learning methodsUtilize the full potential of GPU for data science tasks Handle large and complex data sets efficiently Who This Book Is For Data scientists, data analysts, machine learning engineers, Artificial intelligence practitioners, statisticians who want to take full advantage of Python ecosystem.
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R 197.00
Mechanical Principles of Engineering and Architecture
Seventh wrangler in the Cambridge mathematical tripos in 1826, Henry Moseley (1801–72) was adept at applying mathematical analysis to a wide variety of problems. Appointed professor of natural and experimental philosophy and astronomy at London's newly established King's College in 1831, he was instrumental in creating the institution's department of engineering and applied science. This 1843 textbook is based on the lectures in statics, dynamics and structures that he gave to students of engineering and architecture. Moseley draws on the latest continental work in mechanics, and the treatment of problems is mathematically sophisticated. Starting with basic statics and dynamics, Moseley covers topics of interest to both civil and military engineers, with sections on the theory of machines and on the stability of walls, arches and other structures. Notably, the American edition of this work was adopted as a textbook by the United States Military Academy at West Point.
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Practical Smart Device Design and Construction
With the rapid development of the Internet of Things, a gap has emerged in skills versus knowledge in an industry typically segmented into hardware versus software. Practitioners are now expected to possess capabilities across the spectrum of hardware and software skills to create these smart devices.This book explores these skill sets in an instructive way, beginning at the foundations of what makes “smart” technology smart, addressing the basics of hardware and hardware design, software, user experiences, and culminating in the considerations and means of building a fully formed smart device, capable of being used in a commercial capacity, versus a DIY project. Practical Smart Device Design and Construction includes a set of starter projects designed to encourage the novice to build and learn from doing. Each project also includes a summary guiding you where to go next, and how to tie the practical, hands-on experience together with what they have learned to take the next step on their own.What You'll LearnPractical smart device design and construction considerations such as size, power consumption, wiring needs, analog vs digital, and sensor types and usesMethods and tools for creating their own designs such as circuit board designs; and wiring and prototyping toolsHands-on guidance through their own prototype projects and building it alongside the projects in this bookSoftware considerations for speed versus ease, security, and basics of programming and data analytics for smart devicesWho This Book Is ForThose with some technical skills, or at least a familiarity with technical topics, who are looking for the means and skills to start experimenting with combined hardware and software projects in order to gain familiarity and comfort with the smart device space.
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Generative AI with Python and TensorFlow 2
Fun and exciting projects to learn what artificial minds can createKey FeaturesCode examples are in TensorFlow 2, which make it easy for PyTorch users to follow alongLook inside the most famous deep generative models, from GPT to MuseGANLearn to build and adapt your own models in TensorFlow 2.xExplore exciting, cutting-edge use cases for deep generative AIBook DescriptionMachines are excelling at creative human skills such as painting, writing, and composing music. Could you be more creative than generative AI?In this book, you’ll explore the evolution of generative models, from restricted Boltzmann machines and deep belief networks to VAEs and GANs. You’ll learn how to implement models yourself in TensorFlow and get to grips with the latest research on deep neural networks.There’s been an explosion in potential use cases for generative models. You’ll look at Open AI’s news generator, deepfakes, and training deep learning agents to navigate a simulated environment. Recreate the code that’s under the hood and uncover surprising links between text, image, and music generation.What you will learnExport the code from GitHub into Google Colab to see how everything works for yourselfCompose music using LSTM models, simple GANs, and MuseGANCreate deepfakes using facial landmarks, autoencoders, and pix2pix GANLearn how attention and transformers have changed NLPBuild several text generation pipelines based on LSTMs, BERT, and GPT-2Implement paired and unpaired style transfer with networks like StyleGANDiscover emerging applications of generative AI like folding proteins and creating videos from imagesWho this book is forThis is a book for Python programmers who are keen to create and have some fun using generative models. To make the most out of this book, you should have a basic familiarity with math and statistics for machine learning.
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Computational Physics - A Practical Introduction to Computational Physics and Scientific Computing (Using C++), Vol. I
This book is an introduction to the computational methods used in physics and other related scientific fields. It is addressed to an audience that has already been exposed to the introductory level of college physics, usually taught during the first two years of an undergraduate program in science and engineering. It assumes no prior knowledge of numerical analysis, programming or computers and teaches whatever is necessary for the solution of the problems addressed in the text. C++ is used for programming the core programs and data analysis is performed using the powerful tools of the GNU/Linux environment. All the necessary software is open source and freely available. The book starts with very simple problems in particle motion and ends with an in-depth discussion of advanced techniques used in Monte Carlo simulations in statistical mechanics. The level of instruction rises slowly, while discussing problems like the diffusion equation, electrostatics on the plane, quantum mechanics and random walks.
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Machine Learning Concepts with Python and the Jupyter Notebook Environment
Create, execute, modify, and share machine learning applications with Python and TensorFlow 2.0 in the Jupyter Notebook environment. This book breaks down any barriers to programming machine learning applications through the use of Jupyter Notebook instead of a text editor or a regular IDE.You’ll start by learning how to use Jupyter Notebooks to improve the way you program with Python. After getting a good grounding in working with Python in Jupyter Notebooks, you’ll dive into what TensorFlow is, how it helps machine learning enthusiasts, and how to tackle the challenges it presents. Along the way, sample programs created using Jupyter Notebooks allow you to apply concepts from earlier in the book.Those who are new to machine learning can dive in with these easy programs and develop basic skills. A glossary at the end of the book provides common machine learning and Python keywords and definitions to make learning even easier. What You Will LearnProgram in Python and TensorFlowTackle basic machine learning obstaclesDevelop in the Jupyter Notebooks environmentWho This Book Is ForIdeal for Machine Learning and Deep Learning enthusiasts who are interested in programming with Python using Tensorflow 2.0 in the Jupyter Notebook Application. Some basic knowledge of Machine Learning concepts and Python Programming (using Python version 3) is helpful.
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Data Visualization with Python and JavaScript 2e
How do you turn raw, unprocessed, or malformed data into dynamic, interactive web visualizations? In this practical book, author Kyran Dale shows data scientists and analysts--as well as Python and JavaScript developers--how to create the ideal toolchain for the job. By providing engaging examples and stressing hard-earned best practices, this guide teaches you how to leverage the power of best-of-breed Python and JavaScript libraries. Python provides accessible, powerful, and mature libraries for scraping, cleaning, and processing data. And while JavaScript is the best language when it comes to programming web visualizations, its data processing abilities can't compare with Python's. Together, these two languages are a perfect complement for creating a modern web-visualization toolchain. This book gets you started. You'll learn how to: Obtain data you need programmatically, using scraping tools or web APIs: Requests, Scrapy, Beautiful Soup Clean and process data using Python's heavyweight data processing libraries within the NumPy ecosystem: Jupyter notebooks with pandas+Matplotlib+Seaborn Deliver the data to a browser with static files or by using Flask, the lightweight Python server, and a RESTful API Pick up enough web development skills (HTML, CSS, JS) to get your visualized data on the web Use the data you've mined and refined to create web charts and visualizations with Plotly, D3, Leaflet, and other libraries
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Learn coding with Python and JavaScript
Whether on the computer, tablet, mobile phone, in the car or in the coffee machine - computer programs determine our everyday life. Software is becoming increasingly important, hardly anything works without the mysterious power of algorithms. But how do programs work? And how do you develop them? This book teaches you the basics of programming. Using everyday examples, you will first learn the basic concepts of programming, which are similar in all programming languages. Based on these basic ideas, you will then learn two popular and very useful programming languages, Python and JavaScript, in a systematic way and with many practical exercises, which you can use for a wide range of different tasks. The book is aimed at novice programmers of all ages (from students to professionals) who have no previous programming experience.
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R 580.00
Advanced Analytics in Power BI with R and Python
This easy-to-follow guide provides R and Python recipes to help you learn and apply the top languages in the field of data analytics to your work in Microsoft Power BI. Data analytics expert and author Ryan Wade shows you how to use R and Python to perform tasks that are extremely hard, if not impossible, to do using native Power BI tools. For example, you will learn to score Power BI data using custom data science models and powerful models from Microsoft Cognitive Services. The R and Python languages are powerful complements to Power BI. They enable advanced data transformation techniques that are difficult to perform in Power BI in its default configuration but become easier by leveraging the capabilities of R and Python. If you are a business analyst, data analyst, or a data scientist who wants to push Power BI and transform it from being just a business intelligence tool into an advanced data analytics tool, then this is the book to help you do that. What You Will LearnCreate advanced data visualizations via R using the ggplot2 packageIngest data using R and Python to overcome some limitations of Power QueryApply machine learning models to your data using R and Python without the need of Power BI premium capacityIncorporate advanced AI in Power BI without the need of Power BI premium capacity via Microsoft Cognitive Services, IBM Watson Natural Language Understanding, and pre-trained models in SQL Server Machine Learning Services Perform advanced string manipulations not otherwise possible in Power BI using R and Python Who This Book Is ForPower users, data analysts, and data scientists who want to go beyond Power BI’s built-in functionality to create advanced visualizations, transform data in ways not otherwise supported, and automate data ingestion from sources such as SQL Server and Excel in a more concise way
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Practical Signal Processing And Its Applications: With Solved Homework Problems
This textbook gives a fresh approach to an introductory course in signal processing. Its unique feature is to alternate chapters on continuous-time (analog) and discrete-time (digital) signal processing concepts in a parallel and synchronized manner. This presentation style helps readers to realize and understand the close relationships between continuous and discrete time signal processing, and lays a solid foundation for the study of practical applications such as the analysis and design of analog and digital filters.The compendium provides motivation and necessary mathematical rigor. It generalizes the Fourier transform to Laplace and Z transforms, applies these transforms to linear system analysis, covers the time and frequency-domain analysis of differential and difference equations, and presents practical applications of these techniques to convince readers of their usefulness. MATLAB® examples are provided throughout, and over 100 pages of solved homework problems are included in the appendix.
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